Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations1460
Missing cells455
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory559.0 KiB
Average record size in memory392.1 B

Variable types

Numeric17
Categorical31
Boolean1

Alerts

1stFlrSF is highly overall correlated with SalePrice and 1 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with GrLivAreaHigh correlation
BldgType is highly overall correlated with MSSubClassHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtQual is highly overall correlated with Neighborhood and 2 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
ExterQual is highly overall correlated with OverallQualHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
Foundation is highly overall correlated with YearBuiltHigh correlation
GrLivArea is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotArea and 1 other fieldsHigh correlation
MSSubClass is highly overall correlated with BldgType and 2 other fieldsHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
Neighborhood is highly overall correlated with BsmtQual and 1 other fieldsHigh correlation
OverallQual is highly overall correlated with BsmtQual and 5 other fieldsHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 5 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
Utilities is highly overall correlated with LotFrontageHigh correlation
YearBuilt is highly overall correlated with BsmtQual and 4 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
MSZoning is highly imbalanced (56.9%) Imbalance
Street is highly imbalanced (96.2%) Imbalance
LandContour is highly imbalanced (68.3%) Imbalance
Utilities is highly imbalanced (99.2%) Imbalance
LandSlope is highly imbalanced (78.8%) Imbalance
Condition1 is highly imbalanced (71.7%) Imbalance
Condition2 is highly imbalanced (96.4%) Imbalance
BldgType is highly imbalanced (59.4%) Imbalance
RoofStyle is highly imbalanced (65.1%) Imbalance
RoofMatl is highly imbalanced (94.4%) Imbalance
ExterCond is highly imbalanced (72.8%) Imbalance
BsmtCond is highly imbalanced (75.8%) Imbalance
BsmtFinType2 is highly imbalanced (70.1%) Imbalance
Heating is highly imbalanced (92.7%) Imbalance
CentralAir is highly imbalanced (65.3%) Imbalance
Electrical is highly imbalanced (78.2%) Imbalance
BsmtHalfBath is highly imbalanced (79.7%) Imbalance
LotFrontage has 259 (17.7%) missing values Missing
BsmtQual has 37 (2.5%) missing values Missing
BsmtCond has 37 (2.5%) missing values Missing
BsmtExposure has 38 (2.6%) missing values Missing
BsmtFinType1 has 37 (2.5%) missing values Missing
BsmtFinType2 has 38 (2.6%) missing values Missing
MasVnrArea has 861 (59.0%) zeros Zeros
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtFinSF2 has 1293 (88.6%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 829 (56.8%) zeros Zeros
LowQualFinSF has 1434 (98.2%) zeros Zeros

Reproduction

Analysis started2024-12-18 12:26:20.384121
Analysis finished2024-12-18 12:27:41.203595
Duration1 minute and 20.82 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

SalePrice
Real number (ℝ)

High correlation 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:41.426332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2024-12-18T12:27:41.746477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

MSSubClass
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:41.998805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2024-12-18T12:27:42.218215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

MSZoning
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0342466
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1151
78.8%
RM 218
 
14.9%
FV 65
 
4.5%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2024-12-18T12:27:42.473533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:42.728273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rl 1151
78.3%
rm 218
 
14.8%
fv 65
 
4.4%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotFrontage
Real number (ℝ)

High correlation  Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:42.999028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2024-12-18T12:27:43.304180image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

High correlation 

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:43.600386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2024-12-18T12:27:43.900584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Street
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Pave
1454 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1454
99.6%
Grvl 6
 
0.4%

Length

2024-12-18T12:27:44.164916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:44.366338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pave 1454
99.6%
grvl 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
925 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 925
63.4%
IR1 484
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2024-12-18T12:27:44.590741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:44.809154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
reg 925
63.4%
ir1 484
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

LandContour
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Lvl
1311 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1311
89.8%
Bnk 63
 
4.3%
HLS 50
 
3.4%
Low 36
 
2.5%

Length

2024-12-18T12:27:45.051507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:45.271954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1311
89.8%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utilities
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
AllPub
1459 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8760
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1459
99.9%
NoSeWa 1
 
0.1%

Length

2024-12-18T12:27:45.525280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:45.733684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1459
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Inside
1052 
Corner
263 
CulDSac
 
94
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.959589
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside 1052
72.1%
Corner 263
 
18.0%
CulDSac 94
 
6.4%
FR2 47
 
3.2%
FR3 4
 
0.3%

Length

2024-12-18T12:27:45.987008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:46.239331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
inside 1052
72.1%
corner 263
 
18.0%
culdsac 94
 
6.4%
fr2 47
 
3.2%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

LandSlope
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gtl
1382 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1382
94.7%
Mod 65
 
4.5%
Sev 13
 
0.9%

Length

2024-12-18T12:27:46.492654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:46.702097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1382
94.7%
mod 65
 
4.5%
sev 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Neighborhood
Categorical

High correlation 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.4945205
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 225
15.4%
CollgCr 150
 
10.3%
OldTown 113
 
7.7%
Edwards 100
 
6.8%
Somerst 86
 
5.9%
Gilbert 79
 
5.4%
NridgHt 77
 
5.3%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%
SawyerW 59
 
4.0%
Other values (15) 424
29.0%

Length

2024-12-18T12:27:46.954454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 225
15.4%
collgcr 150
 
10.3%
oldtown 113
 
7.7%
edwards 100
 
6.8%
somerst 86
 
5.9%
gilbert 79
 
5.4%
nridght 77
 
5.3%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 424
29.0%

Most occurring characters

ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9482
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Condition1
Categorical

Imbalance 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1260 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.1212329
Min length4

Characters and Unicode

Total characters6017
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1260
86.3%
Feedr 81
 
5.5%
Artery 48
 
3.3%
RRAn 26
 
1.8%
PosN 19
 
1.3%
RRAe 11
 
0.8%
PosA 8
 
0.5%
RRNn 5
 
0.3%
RRNe 2
 
0.1%

Length

2024-12-18T12:27:47.243646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:47.517914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
norm 1260
86.3%
feedr 81
 
5.5%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 8
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Condition2
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1445 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.0068493
Min length4

Characters and Unicode

Total characters5850
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1445
99.0%
Feedr 6
 
0.4%
Artery 2
 
0.1%
RRNn 2
 
0.1%
PosN 2
 
0.1%
PosA 1
 
0.1%
RRAn 1
 
0.1%
RRAe 1
 
0.1%

Length

2024-12-18T12:27:47.833073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:48.100358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
norm 1445
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2993151
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1220
83.6%
TwnhsE 114
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
2.9%
2fmCon 31
 
2.1%

Length

2024-12-18T12:27:48.398561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:48.647895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1220
83.6%
twnhse 114
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
2.9%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6277
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

HouseStyle
Categorical

High correlation 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9109589
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 726
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.5%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2024-12-18T12:27:48.983996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:49.327078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1story 726
49.7%
2story 445
30.5%
1.5fin 154
 
10.5%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

OverallQual
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:49.583391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2024-12-18T12:27:49.793863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:49.992298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2024-12-18T12:27:50.251606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

YearBuilt
Real number (ℝ)

High correlation 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:50.524874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2024-12-18T12:27:50.825072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:51.137237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2024-12-18T12:27:51.437435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

RoofStyle
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6226027
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1141
78.2%
Hip 286
 
19.6%
Flat 13
 
0.9%
Gambrel 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2024-12-18T12:27:51.742619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:52.006915image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gable 1141
78.2%
hip 286
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

RoofMatl
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
CompShg
1434 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.9965753
Min length4

Characters and Unicode

Total characters10215
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1434
98.2%
Tar&Grv 11
 
0.8%
WdShngl 6
 
0.4%
WdShake 5
 
0.3%
Metal 1
 
0.1%
Membran 1
 
0.1%
Roll 1
 
0.1%
ClyTile 1
 
0.1%

Length

2024-12-18T12:27:52.286166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:52.545473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1434
98.2%
tar&grv 11
 
0.8%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%
clytile 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Exterior1st
Categorical

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.9794521
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 515
35.3%
HdBoard 222
15.2%
MetalSd 220
15.1%
Wd Sdng 206
 
14.1%
Plywood 108
 
7.4%
CemntBd 61
 
4.2%
BrkFace 50
 
3.4%
WdShing 26
 
1.8%
Stucco 25
 
1.7%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2024-12-18T12:27:52.863622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 515
30.9%
hdboard 222
13.3%
metalsd 220
13.2%
wd 206
 
12.4%
sdng 206
 
12.4%
plywood 108
 
6.5%
cemntbd 61
 
3.7%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Exterior2nd
Categorical

High correlation 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
504 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.9732877
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 504
34.5%
MetalSd 214
14.7%
HdBoard 207
14.2%
Wd Sdng 197
 
13.5%
Plywood 142
 
9.7%
CmentBd 60
 
4.1%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
BrkFace 25
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 27
 
1.8%

Length

2024-12-18T12:27:53.178780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 504
29.6%
wd 235
13.8%
metalsd 214
12.6%
hdboard 207
12.2%
sdng 197
 
11.6%
plywood 142
 
8.3%
cmentbd 60
 
3.5%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10181
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:53.455043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2024-12-18T12:27:53.759227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

ExterQual
Categorical

High correlation 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.1%
Gd 488
33.4%
Ex 52
 
3.6%
Fa 14
 
1.0%

Length

2024-12-18T12:27:54.044466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:54.261884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.1%
gd 488
33.4%
ex 52
 
3.6%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1282
87.8%
Gd 146
 
10.0%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2024-12-18T12:27:54.526177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:54.750613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1282
87.8%
gd 146
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.5157534
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 647
44.3%
CBlock 634
43.4%
BrkTil 146
 
10.0%
Slab 24
 
1.6%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2024-12-18T12:27:55.023849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:55.281159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pconc 647
44.3%
cblock 634
43.4%
brktil 146
 
10.0%
slab 24
 
1.6%
stone 6
 
0.4%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtQual
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
649 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 649
44.5%
Gd 618
42.3%
Ex 121
 
8.3%
Fa 35
 
2.4%
(Missing) 37
 
2.5%

Length

2024-12-18T12:27:55.571386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:55.789799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 649
45.6%
gd 618
43.4%
ex 121
 
8.5%
fa 35
 
2.5%

Most occurring characters

ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

BsmtCond
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
1311 
Gd
 
65
Fa
 
45
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1311
89.8%
Gd 65
 
4.5%
Fa 45
 
3.1%
Po 2
 
0.1%
(Missing) 37
 
2.5%

Length

2024-12-18T12:27:56.063116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:56.283490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ta 1311
92.1%
gd 65
 
4.6%
fa 45
 
3.2%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1311
46.1%
A 1311
46.1%
G 65
 
2.3%
d 65
 
2.3%
F 45
 
1.6%
a 45
 
1.6%
P 2
 
0.1%
o 2
 
0.1%

BsmtExposure
Categorical

Missing 

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
No
953 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 953
65.3%
Av 221
 
15.1%
Gd 134
 
9.2%
Mn 114
 
7.8%
(Missing) 38
 
2.6%

Length

2024-12-18T12:27:56.515905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:56.742263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
no 953
67.0%
av 221
 
15.5%
gd 134
 
9.4%
mn 114
 
8.0%

Most occurring characters

ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 953
33.5%
o 953
33.5%
A 221
 
7.8%
v 221
 
7.8%
G 134
 
4.7%
d 134
 
4.7%
M 114
 
4.0%
n 114
 
4.0%

BsmtFinType1
Categorical

Missing 

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
Unf
430 
GLQ
418 
ALQ
220 
BLQ
148 
Rec
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4269
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 430
29.5%
GLQ 418
28.6%
ALQ 220
15.1%
BLQ 148
 
10.1%
Rec 133
 
9.1%
LwQ 74
 
5.1%
(Missing) 37
 
2.5%

Length

2024-12-18T12:27:57.021544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:57.267896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unf 430
30.2%
glq 418
29.4%
alq 220
15.5%
blq 148
 
10.4%
rec 133
 
9.3%
lwq 74
 
5.2%

Most occurring characters

ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 860
20.1%
Q 860
20.1%
U 430
10.1%
n 430
10.1%
f 430
10.1%
G 418
9.8%
A 220
 
5.2%
B 148
 
3.5%
R 133
 
3.1%
e 133
 
3.1%
Other values (2) 207
 
4.8%

BsmtFinSF1
Real number (ℝ)

High correlation  Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:57.551101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2024-12-18T12:27:57.837336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType2
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
Unf
1256 
Rec
 
54
LwQ
 
46
BLQ
 
33
ALQ
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4266
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1256
86.0%
Rec 54
 
3.7%
LwQ 46
 
3.2%
BLQ 33
 
2.3%
ALQ 19
 
1.3%
GLQ 14
 
1.0%
(Missing) 38
 
2.6%

Length

2024-12-18T12:27:58.107616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:27:58.338000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
unf 1256
88.3%
rec 54
 
3.8%
lwq 46
 
3.2%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4266
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4266
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4266
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1256
29.4%
n 1256
29.4%
f 1256
29.4%
L 112
 
2.6%
Q 112
 
2.6%
R 54
 
1.3%
e 54
 
1.3%
c 54
 
1.3%
w 46
 
1.1%
B 33
 
0.8%
Other values (2) 33
 
0.8%

BsmtFinSF2
Real number (ℝ)

Zeros 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549315
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:58.612264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.31927
Coefficient of variation (CV)3.4655563
Kurtosis20.113338
Mean46.549315
Median Absolute Deviation (MAD)0
Skewness4.2552611
Sum67962
Variance26023.908
MonotonicityNot monotonic
2024-12-18T12:27:59.372232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

High correlation  Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:27:59.673429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2024-12-18T12:27:59.969635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

High correlation  Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:28:00.265845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2024-12-18T12:28:00.566674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

Heating
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GasA
1428 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006849
Min length4

Characters and Unicode

Total characters5841
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1428
97.8%
GasW 18
 
1.2%
Grav 7
 
0.5%
Wall 4
 
0.3%
OthW 2
 
0.1%
Floor 1
 
0.1%

Length

2024-12-18T12:28:00.842905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:01.082265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1428
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 741
50.8%
TA 428
29.3%
Gd 241
 
16.5%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2024-12-18T12:28:01.371492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:01.606899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
ex 741
50.8%
ta 428
29.3%
gd 241
 
16.5%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True 1365
93.5%
False 95
 
6.5%
2024-12-18T12:28:01.825315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Electrical
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
SBrkr
1334 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.9986292
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1334
91.4%
FuseA 94
 
6.4%
FuseF 27
 
1.8%
FuseP 3
 
0.2%
Mix 1
 
0.1%
(Missing) 1
 
0.1%

Length

2024-12-18T12:28:02.111513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:02.364836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1334
91.4%
fusea 94
 
6.4%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

1stFlrSF
Real number (ℝ)

High correlation 

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:28:02.625168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2024-12-18T12:28:02.906427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

High correlation  Zeros 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:28:03.215598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2024-12-18T12:28:03.510809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:28:03.753157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2024-12-18T12:28:04.013427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

High correlation 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2024-12-18T12:28:04.293712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2024-12-18T12:28:04.588889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2024-12-18T12:28:04.846235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:05.062657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2024-12-18T12:28:05.300025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:05.533362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2024-12-18T12:28:05.755807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:05.972229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2024-12-18T12:28:06.221564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T12:28:06.459918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Interactions

2024-12-18T12:27:35.153394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:33.290267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:38.576439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:42.562745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:46.485209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:50.251309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:53.959365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:58.232014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:02.210817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:06.561186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:10.626658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.993748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:17.528242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:21.170654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:24.946605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:28.359274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.744259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:35.378647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:33.619391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:38.857685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:42.785155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:46.706617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:50.476676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:54.190745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:58.499621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:02.518993image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:06.838648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:10.843084image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:14.213164image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:17.743670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:21.383088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:25.219876image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:28.567713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.958720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:35.588231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:33.845785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:39.077063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:43.004565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:46.960939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:50.665171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:54.436087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:58.724058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:02.760347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:07.297203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:11.056510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:14.425759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:17.937152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:21.584877image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:25.498131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:28.760199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:32.162143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:35.774733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:34.092124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:39.352329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:43.225976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:47.242456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:50.880592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:54.841007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:58.956400image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:03.036610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:07.541548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:11.253982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:14.630246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:18.126446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:21.786338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:25.684631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:28.961659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:32.356680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:35.951297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:34.329489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:39.582711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:43.409431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:47.483845image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:51.124943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:55.103304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:59.161853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:03.264998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:07.742689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:11.446469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:14.828681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:18.302973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:21.984811image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:25.861162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:29.142212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:32.546116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:36.134806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:34.611769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:39.835039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:43.609896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:47.673302image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:51.311441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:55.313782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:59.424149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:03.536273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:07.937168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:11.626987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:15.027152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:18.484595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:22.175297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:26.035773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:29.320699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:32.735613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:36.340223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:34.940856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:40.098334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:43.867213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:47.947570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:51.571746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:55.556095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:59.654534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:03.798571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:08.203139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:11.839418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:15.268540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:18.694036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:22.395711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:26.253113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:29.535129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:32.951033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:36.544712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:35.192186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:40.307778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:44.105571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:48.171973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:51.844018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:55.833352image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:59.884921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:04.053926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:08.431565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:12.043870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:15.483930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:18.896496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:22.644045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:26.443728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:29.776480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:33.157481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:36.742192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:35.473433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:40.524197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:44.337951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:48.392385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:52.049502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:56.057187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:00.107324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:04.298272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:08.669891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:12.243376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:15.705339image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:19.136853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:22.874414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:26.643190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:30.051744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:33.358943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:36.952663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:35.761660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:40.755577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:44.595267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:48.594840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:52.332711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:56.312506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:00.370732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:04.575495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:08.896320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:12.461879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:15.931733image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:19.349285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:23.094555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:26.849640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:30.258233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:33.578401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:37.495175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:35.966794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:40.968008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:44.782761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:48.788322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:52.547138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:56.590763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:00.620065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:04.827820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:09.111745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:12.662342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:16.131045image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:19.542764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:23.291065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.075037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:30.439750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:33.777896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:37.699792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:36.199757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:41.230309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:45.200644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:49.035664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:52.752622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:56.850064image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:00.921259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:05.036263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:09.336114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:12.870790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:16.345468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:19.749216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:23.507454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.275500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:30.649188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:33.992295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:37.881143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:36.653543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:41.456737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:45.385152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:49.227149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:52.930140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:57.121467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:01.163618image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:05.307539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:09.532583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.057253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:16.535958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:19.926744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:23.697942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.448101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:30.824721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:34.177799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:38.073653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:37.070429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:41.693098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:45.610550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:49.475485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:53.175457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:57.335889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:01.371062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:05.564853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:09.760010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.255723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:16.741384image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:20.140170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:23.900405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.657185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.018204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:34.390236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:38.241179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:37.461383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:41.872629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:45.819990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:49.651052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:53.373928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:57.586774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:01.575518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:05.764317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:09.964430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.427264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:16.927850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:20.326909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:24.083910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.823736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.182761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:34.571746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:38.415715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:37.978003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:42.103971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:46.046383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:49.832497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:53.550458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:57.775235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:01.768998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:06.057534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:10.166891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.605785image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:17.123326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:20.798649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:24.389095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:27.994247image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.363277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:34.757867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:38.612189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:38.371951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:42.357296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:46.274773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:50.030867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:53.738951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:26:57.989665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:01.972456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:06.329840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:10.407246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:13.799304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:17.333764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:20.990139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:24.698267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:28.182745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:31.555763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-12-18T12:27:34.959332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-12-18T12:28:06.729166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
1stFlrSF2ndFlrSFBldgTypeBsmtCondBsmtExposureBsmtFinSF1BsmtFinSF2BsmtFinType1BsmtFinType2BsmtFullBathBsmtHalfBathBsmtQualBsmtUnfSFCentralAirCondition1Condition2ElectricalExterCondExterQualExterior1stExterior2ndFoundationFullBathGrLivAreaHalfBathHeatingHeatingQCHouseStyleLandContourLandSlopeLotAreaLotConfigLotFrontageLotShapeLowQualFinSFMSSubClassMSZoningMasVnrAreaNeighborhoodOverallCondOverallQualRoofMatlRoofStyleSalePriceStreetTotalBsmtSFUtilitiesYearBuiltYearRemodAdd
1stFlrSF1.000-0.2760.1840.0000.1760.3230.0670.1100.0100.1910.0000.2640.2240.1440.0860.1910.0190.0000.2710.1820.1550.0880.2580.4940.1210.0000.0940.1600.0960.0000.4440.0570.4280.207-0.039-0.2780.1600.3520.241-0.1670.4090.3940.1500.5750.0000.8290.0000.2930.240
2ndFlrSF-0.2761.0000.1250.0550.153-0.191-0.1020.1330.0110.1250.0000.2060.0600.0360.0520.1100.0000.0320.2090.1010.1320.1570.4070.6430.4490.0430.1120.4350.0580.0000.1190.0520.0550.1480.0580.4880.1590.0630.2500.0010.2900.1060.1200.2940.000-0.2860.0000.0300.073
BldgType0.1840.1251.0000.0410.0750.0000.0000.0990.0210.1600.0650.1630.1220.2890.0760.1440.0810.1100.1760.1630.1870.1880.1050.0490.2290.1070.1120.1560.0690.0260.0360.0690.3130.0840.0760.8510.1890.0000.4190.1280.1310.0270.0480.0880.1120.1200.0000.2500.195
BsmtCond0.0000.0550.0411.0000.0550.0000.0000.1020.0520.0760.0580.1930.0550.2600.0380.0000.4230.2220.1630.1120.0740.1380.1440.0000.0430.0700.0920.0840.0630.1350.0000.0290.0000.0450.0960.0690.0930.0000.1210.4760.4260.0470.0550.1230.0000.0350.0000.1800.109
BsmtExposure0.1760.1530.0750.0551.0000.2340.0640.1990.0800.2000.0450.1950.1270.0760.0700.0000.0590.0000.1550.1240.1410.1280.0990.1200.0700.0000.0810.2300.1970.2230.1450.0680.1150.1010.0000.2010.0750.0950.2700.0960.1810.1490.1380.2060.0900.1880.0000.1780.151
BsmtFinSF10.323-0.1910.0000.0000.2341.0000.0500.2710.0520.3970.0280.241-0.5740.1520.1070.3120.0570.0000.2080.1380.1390.1110.1580.0570.0120.0000.0590.1140.1380.0830.1720.0450.1540.206-0.079-0.1080.0910.2420.201-0.0110.1330.4490.0960.3020.0190.4100.0000.1900.063
BsmtFinSF20.067-0.1020.0000.0000.0640.0501.0000.1910.4710.0880.0880.063-0.2710.0000.0000.0390.0000.0000.0410.0720.0630.0700.036-0.0520.0080.0000.0000.0000.0490.1410.0720.0000.0530.0560.002-0.0840.000-0.0610.1230.102-0.1180.1540.134-0.0390.0490.0700.184-0.112-0.126
BsmtFinType10.1100.1330.0990.1020.1990.2710.1911.0000.2130.3380.0810.3330.2950.1750.0460.0250.1000.0610.2920.2160.2230.3150.2260.1080.0540.0640.2020.1610.0880.0470.0130.0590.0520.0510.0710.1820.1360.0960.3130.1710.2310.0470.0560.2070.0000.1320.0000.3510.263
BsmtFinType20.0100.0110.0210.0520.0800.0520.4710.2131.0000.0750.0910.1000.1190.0080.0160.0230.0000.0000.0930.1310.1180.1240.0470.0000.0350.0000.0780.0550.0000.0780.0670.0000.0280.0570.0000.0540.0390.0000.1570.0600.0750.1110.0750.0510.1070.0240.1200.1330.113
BsmtFullBath0.1910.1250.1600.0760.2000.3970.0880.3380.0751.0000.0970.1020.2650.1060.0000.0000.0550.0000.0710.0890.0830.1030.2640.1360.1520.0000.0600.1650.1050.2000.2110.0000.1550.0940.0000.2090.0710.0280.1890.0000.0660.1660.1250.1400.0910.2040.0000.1440.122
BsmtHalfBath0.0000.0000.0650.0580.0450.0280.0880.0810.0910.0971.0000.0520.0540.0160.0000.0000.0000.0590.0430.0660.0680.0670.1640.0000.1540.0000.0300.0990.0270.0450.0000.0340.0000.0450.0000.0860.0200.0000.1450.1020.0620.1390.1270.0490.0000.0000.1020.0890.077
BsmtQual0.2640.2060.1630.1930.1950.2410.0630.3330.1000.1020.0521.0000.1850.2140.1510.1090.2180.1110.4620.3240.3160.4050.3470.2490.1540.0270.2710.2120.0940.0000.0000.0860.1250.1360.0570.2660.1910.2090.5350.3230.5100.0390.1670.4550.0000.2930.0000.5160.392
BsmtUnfSF0.2240.0600.1220.0550.127-0.574-0.2710.2950.1190.2650.0540.1851.0000.0600.0160.0620.0380.0260.2510.0940.1020.1700.1870.2530.1220.0000.0980.1500.0640.0530.0780.0120.1190.0390.020-0.1180.0720.0760.191-0.1280.2730.0000.0910.1850.0000.3290.0000.1390.177
CentralAir0.1440.0360.2890.2600.0760.1520.0000.1750.0080.1060.0160.2140.0601.0000.0400.0680.4210.2000.2780.3490.3300.3650.1030.1580.1300.4610.3790.2330.1280.0000.0000.0630.0520.1080.1260.2540.2970.1060.3820.3150.3740.0000.0550.4180.0400.2230.0000.4380.378
Condition10.0860.0520.0760.0380.0700.1070.0000.0460.0160.0000.0000.1510.0160.0401.0000.2100.0400.0190.1250.0720.0790.0800.0620.0870.0870.0000.1570.0850.0000.0000.0000.1480.1470.1050.0000.1030.0710.0000.1850.0510.0610.0770.0810.0650.1650.0830.0000.1200.080
Condition20.1910.1100.1440.0000.0000.3120.0390.0250.0230.0000.0000.1090.0620.0680.2101.0000.0000.2840.1370.0230.0000.0350.1060.2710.1990.0000.0690.1220.0590.0000.0000.0920.0770.0000.0840.1030.0590.1020.0100.0990.1530.0000.3110.0000.0000.1440.0000.1620.000
Electrical0.0190.0000.0810.4230.0590.0570.0000.1000.0000.0550.0000.2180.0380.4210.0400.0001.0000.1280.1380.2040.1800.1820.1160.0080.0820.1320.1440.1090.0520.0000.0000.0070.0000.1120.0000.1180.1030.0000.1790.2700.1600.0000.0000.1350.0000.0640.0850.1880.221
ExterCond0.0000.0320.1100.2220.0000.0000.0000.0610.0000.0000.0590.1110.0260.2000.0190.2840.1281.0000.1820.0950.0670.1230.0750.0530.0510.0460.0620.1030.0000.0000.0000.0000.0000.0000.0870.1150.0790.0000.1530.3790.1950.0000.0920.1050.0000.0380.0000.1890.099
ExterQual0.2710.2090.1760.1630.1550.2080.0410.2920.0930.0710.0430.4620.2510.2780.1250.1370.1380.1821.0000.3510.3550.3710.3180.2860.1510.0420.3240.1760.1340.0940.0000.0140.1380.1120.0900.2430.2390.2440.4850.3190.6140.0660.1460.4760.3210.3190.0000.4350.389
Exterior1st0.1820.1010.1630.1120.1240.1380.0720.2160.1310.0890.0660.3240.0940.3490.0720.0230.2040.0950.3511.0000.7590.3150.2370.1110.1200.1340.2660.1600.1160.1340.0260.0520.1130.0820.0000.1880.1780.0100.2880.1890.2000.1860.1380.1640.0000.1340.0000.3350.285
Exterior2nd0.1550.1320.1870.0740.1410.1390.0630.2230.1180.0830.0680.3160.1020.3300.0790.0000.1800.0670.3550.7591.0000.3140.2250.1230.1660.1740.2650.1670.1210.1160.0710.0760.1290.0930.0000.2100.1860.0280.3170.1690.1930.1160.1600.1750.0000.1440.0000.3260.277
Foundation0.0880.1570.1880.1380.1280.1110.0700.3150.1240.1030.0670.4050.1700.3650.0800.0350.1820.1230.3710.3150.3141.0000.2850.1520.1640.2160.2930.2160.1000.0500.0000.0430.1150.1170.0270.2630.2240.0770.4170.2560.2910.0000.0920.2580.0440.2330.0000.5020.322
FullBath0.2580.4070.1050.1440.0990.1580.0360.2260.0470.2640.1640.3470.1870.1030.0620.1060.1160.0750.3180.2370.2250.2851.0000.4680.2300.0000.1990.2360.1110.1240.0980.0410.1350.1020.0000.2490.1750.1820.3690.3090.4040.1060.1390.4160.0220.2360.0000.3510.270
GrLivArea0.4940.6430.0490.0000.1200.057-0.0520.1080.0000.1360.0000.2490.2530.1580.0870.2710.0080.0530.2860.1110.1230.1520.4681.0000.3000.0520.1430.2580.1000.0360.4490.0460.3760.2220.0640.2040.1060.3230.209-0.1540.6030.4060.0620.7310.0000.3710.0000.2880.282
HalfBath0.1210.4490.2290.0430.0700.0120.0080.0540.0350.1520.1540.1540.1220.1300.0870.1990.0820.0510.1510.1200.1660.1640.2300.3001.0000.0000.0970.4610.0030.0410.0000.0000.0330.0840.0000.5120.1400.1380.3000.0790.2250.0180.2100.2080.0000.0970.0000.2270.200
Heating0.0000.0430.1070.0700.0000.0000.0000.0640.0000.0000.0000.0270.0000.4610.0000.0000.1320.0460.0420.1340.1740.2160.0000.0520.0001.0000.2390.1410.0000.0000.0840.0000.0460.0250.3080.1050.0550.0000.0540.0920.1670.0000.0000.0810.0000.0730.0000.1710.083
HeatingQC0.0940.1120.1120.0920.0810.0590.0000.2020.0780.0600.0300.2710.0980.3790.1570.0690.1440.0620.3240.2660.2650.2930.1990.1430.0970.2391.0000.1680.0540.0500.0000.0100.0490.0530.0590.1680.1170.0380.2960.1780.2590.0000.0000.2380.0180.1400.0270.3360.328
HouseStyle0.1600.4350.1560.0840.2300.1140.0000.1610.0550.1650.0990.2120.1500.2330.0850.1220.1090.1030.1760.1600.1670.2160.2360.2580.4610.1410.1681.0000.1260.0000.0000.0000.0440.0730.2630.6170.1840.0470.2940.1220.1440.0470.1020.1290.0190.1640.1000.2910.200
LandContour0.0960.0580.0690.0630.1970.1380.0490.0880.0000.1050.0270.0940.0640.1280.0000.0590.0520.0000.1340.1160.1210.1000.1110.1000.0030.0000.0540.1261.0000.4570.2560.0600.1210.1270.0730.0840.1020.0250.3600.1010.1610.1810.1410.0960.1140.1060.0000.1600.130
LandSlope0.0000.0000.0260.1350.2230.0830.1410.0470.0780.2000.0450.0000.0530.0000.0000.0000.0000.0000.0940.1340.1160.0500.1240.0360.0410.0000.0500.0000.4571.0000.4490.0790.1230.1190.0530.0000.0720.0000.3150.1880.1520.3130.2560.0460.1760.0000.0000.0980.083
LotArea0.4440.1190.0360.0000.1450.1720.0720.0130.0670.2110.0000.0000.0780.0000.0000.0000.0000.0000.0000.0260.0710.0000.0980.4490.0000.0840.0000.0000.2560.4491.0000.0790.6500.266-0.020-0.2700.0000.1780.162-0.0470.2330.2520.1120.4560.2900.3660.0000.1030.075
LotConfig0.0570.0520.0690.0290.0680.0450.0000.0590.0000.0000.0340.0860.0120.0630.1480.0920.0070.0000.0140.0520.0760.0430.0410.0460.0000.0000.0100.0000.0600.0790.0791.0000.1650.2210.0000.0620.0640.0360.1370.0000.0170.0770.0750.0870.0000.0290.0850.1050.086
LotFrontage0.4280.0550.3130.0000.1150.1540.0530.0520.0280.1550.0000.1250.1190.0520.1470.0770.0000.0000.1380.1130.1290.1150.1350.3760.0330.0460.0490.0440.1210.1230.6500.1651.0000.297-0.030-0.3140.1930.2590.245-0.0830.2550.3050.1540.4090.1130.3861.0000.1950.117
LotShape0.2070.1480.0840.0450.1010.2060.0560.0510.0570.0940.0450.1360.0390.1080.1050.0000.1120.0000.1120.0820.0930.1170.1020.2220.0840.0250.0530.0730.1270.1190.2660.2210.2971.0000.0000.1380.1520.0690.2440.0600.1160.1860.0350.1970.0340.2000.0000.1740.139
LowQualFinSF-0.0390.0580.0760.0960.000-0.0790.0020.0710.0000.0000.0000.0570.0200.1260.0000.0840.0000.0870.0900.0000.0000.0270.0000.0640.0000.3080.0590.2630.0730.053-0.0200.000-0.0300.0001.0000.0760.145-0.1070.1120.040-0.0340.0490.000-0.0680.000-0.0810.000-0.146-0.065
MSSubClass-0.2780.4880.8510.0690.201-0.108-0.0840.1820.0540.2090.0860.266-0.1180.2540.1030.1030.1180.1150.2430.1880.2100.2630.2490.2040.5120.1050.1680.6170.0840.000-0.2700.062-0.3140.1380.0761.0000.2640.0250.422-0.0720.1080.0390.1170.0070.103-0.3190.0000.0360.007
MSZoning0.1600.1590.1890.0930.0750.0910.0000.1360.0390.0710.0200.1910.0720.2970.0710.0590.1030.0790.2390.1780.1860.2240.1750.1060.1400.0550.1170.1840.1020.0720.0000.0640.1930.1520.1450.2641.0000.0630.6410.1610.1900.0000.0730.2060.2490.1190.0000.2950.202
MasVnrArea0.3520.0630.0000.0000.0950.242-0.0610.0960.0000.0280.0000.2090.0760.1060.0000.1020.0000.0000.2440.0100.0280.0770.1820.3230.1380.0000.0380.0470.0250.0000.1780.0360.2590.069-0.1070.0250.0631.0000.183-0.1790.4140.1420.1050.4210.0000.3600.1700.4020.234
Neighborhood0.2410.2500.4190.1210.2700.2010.1230.3130.1570.1890.1450.5350.1910.3820.1850.0100.1790.1530.4850.2880.3170.4170.3690.2090.3000.0540.2960.2940.3600.3150.1620.1370.2450.2440.1120.4220.6410.1831.0000.2220.3210.0950.1860.3190.1990.2370.0960.4800.388
OverallCond-0.1670.0010.1280.4760.096-0.0110.1020.1710.0600.0000.1020.323-0.1280.3150.0510.0990.2700.3790.3190.1890.1690.2560.309-0.1540.0790.0920.1780.1220.1010.188-0.0470.000-0.0830.0600.040-0.0720.161-0.1790.2221.000-0.1780.0000.044-0.1290.068-0.2170.000-0.417-0.041
OverallQual0.4090.2900.1310.4260.1810.133-0.1180.2310.0750.0660.0620.5100.2730.3740.0610.1530.1600.1950.6140.2000.1930.2910.4040.6030.2250.1670.2590.1440.1610.1520.2330.0170.2550.116-0.0340.1080.1900.4140.321-0.1781.0000.0990.1170.8100.0730.4600.0000.6470.558
RoofMatl0.3940.1060.0270.0470.1490.4490.1540.0470.1110.1660.1390.0390.0000.0000.0770.0000.0000.0000.0660.1860.1160.0000.1060.4060.0180.0000.0000.0470.1810.3130.2520.0770.3050.1860.0490.0390.0000.1420.0950.0000.0991.0000.4580.1210.0000.4240.0000.0710.040
RoofStyle0.1500.1200.0480.0550.1380.0960.1340.0560.0750.1250.1270.1670.0910.0550.0810.3110.0000.0920.1460.1380.1600.0920.1390.0620.2100.0000.0000.1020.1410.2560.1120.0750.1540.0350.0000.1170.0730.1050.1860.0440.1170.4581.0000.1130.0000.1230.0000.1600.081
SalePrice0.5750.2940.0880.1230.2060.302-0.0390.2070.0510.1400.0490.4550.1850.4180.0650.0000.1350.1050.4760.1640.1750.2580.4160.7310.2080.0810.2380.1290.0960.0460.4560.0870.4090.197-0.0680.0070.2060.4210.319-0.1290.8100.1210.1131.0000.0000.6030.0000.6530.571
Street0.0000.0000.1120.0000.0900.0190.0490.0000.1070.0910.0000.0000.0000.0400.1650.0000.0000.0000.3210.0000.0000.0440.0220.0000.0000.0000.0180.0190.1140.1760.2900.0000.1130.0340.0000.1030.2490.0000.1990.0680.0730.0000.0000.0001.0000.0000.0000.0000.110
TotalBsmtSF0.829-0.2860.1200.0350.1880.4100.0700.1320.0240.2040.0000.2930.3290.2230.0830.1440.0640.0380.3190.1340.1440.2330.2360.3710.0970.0730.1400.1640.1060.0000.3660.0290.3860.200-0.081-0.3190.1190.3600.237-0.2170.4600.4240.1230.6030.0001.0000.0000.4270.299
Utilities0.0000.0000.0000.0000.0000.0000.1840.0000.1200.0000.1020.0000.0000.0000.0000.0000.0850.0000.0000.0000.0000.0000.0000.0000.0000.0000.0270.1000.0000.0000.0000.0851.0000.0000.0000.0000.0000.1700.0960.0000.0000.0000.0000.0000.0000.0001.0000.0000.079
YearBuilt0.2930.0300.2500.1800.1780.190-0.1120.3510.1330.1440.0890.5160.1390.4380.1200.1620.1880.1890.4350.3350.3260.5020.3510.2880.2270.1710.3360.2910.1600.0980.1030.1050.1950.174-0.1460.0360.2950.4020.480-0.4170.6470.0710.1600.6530.0000.4270.0001.0000.684
YearRemodAdd0.2400.0730.1950.1090.1510.063-0.1260.2630.1130.1220.0770.3920.1770.3780.0800.0000.2210.0990.3890.2850.2770.3220.2700.2820.2000.0830.3280.2000.1300.0830.0750.0860.1170.139-0.0650.0070.2020.2340.388-0.0410.5580.0400.0810.5710.1100.2990.0790.6841.000

Missing values

2024-12-18T12:27:39.039046image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-18T12:27:40.320620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-18T12:27:40.960627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SalePriceMSSubClassMSZoningLotFrontageLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBath
020850060RL65.08450PaveRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSd196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr856854017101021
118150020RL80.09600PaveRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSd0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr12620012620120
222350060RL68.011250PaveIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSd162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr920866017861021
314000070RL60.09550PaveIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd Shng0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr961756017171010
425000060RL84.014260PaveIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSd350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr11451053021981021
514300050RL85.014115PaveIR1LvlAllPubInsideGtlMitchelNormNorm1Fam1.5Fin5519931995GableCompShgVinylSdVinylSd0.0TATAWoodGdTANoGLQ732Unf064796GasAExYSBrkr796566013621011
630700020RL75.010084PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520042005GableCompShgVinylSdVinylSd186.0GdTAPConcExTAAvGLQ1369Unf03171686GasAExYSBrkr16940016941020
720000060RLNaN10382PaveIR1LvlAllPubCornerGtlNWAmesPosNNorm1Fam2Story7619731973GableCompShgHdBoardHdBoard240.0TATACBlockGdTAMnALQ859BLQ322161107GasAExYSBrkr1107983020901021
812990050RM51.06120PaveRegLvlAllPubInsideGtlOldTownArteryNorm1Fam1.5Fin7519311950GableCompShgBrkFaceWd Shng0.0TATABrkTilTATANoUnf0Unf0952952GasAGdYFuseF1022752017740020
9118000190RL50.07420PaveRegLvlAllPubCornerGtlBrkSideArteryArtery2fmCon1.5Unf5619391950GableCompShgMetalSdMetalSd0.0TATABrkTilTATANoGLQ851Unf0140991GasAExYSBrkr10770010771010
SalePriceMSSubClassMSZoningLotFrontageLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBath
145013600090RL60.09000PaveRegLvlAllPubFR2GtlNAmesNormNormDuplex2Story5519741974GableCompShgVinylSdVinylSd0.0TATACBlockGdTANoUnf0Unf0896896GasATAYSBrkr896896017920022
145128709020RL78.09262PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520082009GableCompShgCemntBdCmentBd194.0GdTAPConcGdTANoUnf0Unf015731573GasAExYSBrkr15780015780020
1452145000180RM35.03675PaveRegLvlAllPubInsideGtlEdwardsNormNormTwnhsESLvl5520052005GableCompShgVinylSdVinylSd80.0TATAPConcGdTAGdGLQ547Unf00547GasAGdYSBrkr10720010721010
14538450020RL90.017217PaveRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story5520062006GableCompShgVinylSdVinylSd0.0TATAPConcGdTANoUnf0Unf011401140GasAExYSBrkr11400011400010
145418500020FV62.07500PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story7520042005GableCompShgVinylSdVinylSd0.0GdTAPConcGdTANoGLQ410Unf08111221GasAExYSBrkr12210012211020
145517500060RL62.07917PaveRegLvlAllPubInsideGtlGilbertNormNorm1Fam2Story6519992000GableCompShgVinylSdVinylSd0.0TATAPConcGdTANoUnf0Unf0953953GasAExYSBrkr953694016470021
145621000020RL85.013175PaveRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story6619781988GableCompShgPlywoodPlywood119.0TATACBlockGdTANoALQ790Rec1635891542GasATAYSBrkr20730020731020
145726650070RL66.09042PaveRegLvlAllPubInsideGtlCrawforNormNorm1Fam2Story7919412006GableCompShgCemntBdCmentBd0.0ExGdStoneTAGdNoGLQ275Unf08771152GasAExYSBrkr11881152023400020
145814212520RL68.09717PaveRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story5619501996HipCompShgMetalSdMetalSd0.0TATACBlockTATAMnGLQ49Rec102901078GasAGdYFuseA10780010781010
145914750020RL75.09937PaveRegLvlAllPubInsideGtlEdwardsNormNorm1Fam1Story5619651965GableCompShgHdBoardHdBoard0.0GdTACBlockTATANoBLQ830LwQ2901361256GasAGdYSBrkr12560012561011